Ziying Tang

Tuning fuzzy membership functions to improve value of information calculations

A major tenet of the US Armys data-to-decision initiative and a primary challenge for military commanders and their staff is the ability to shorten the cycle time from data gathering to making decisions. Paramount to this process is the ability to better assess the applicability and relevance of information for decisions in complex military environments. Towards this end, the Army Research Laboratory, in collaboration with Towson University, has embarked on a research initiative to experimentally characterize how analysts perceive the value of information (VoI) and subsequently model and test solutions. This paper presents the process used to extend the current fuzzy VoI system to allow user-defined membership functions that consider various domain decompositions using both triangular and trapezoidal fuzzy sets, and the assessment of their efficacy to current military operations.

Real-time 3D interaction with deformable model on mobile devices

Mobile-based augmented reality is an emerging technology that provides immersive experiences over wireless networks. Its applications, such as 3D streaming, have become more and more popular on mobile devices. However, most of these applications do not support real-time 3D interactions. Consequently, mobile users can only watch or browse 3D contents passively instead of actively interacting with 3D objects. In this paper, we propose a novel method that allows mobile users to change a models shape and motions through interactions via mobile touch screen and obtain feedbacks in real-time. To accelerate computational speed and reduce communication load, we compute 3D deformations using a spectral representation. Moreover, a progressive deformation streaming technique is proposed to reduce the effect of interaction delay between the server and mobile clients. Our experimental results indicate that our method provides real-time interaction feedback, offering satisfactory user experiences.

ARMStrokes: A Mobile App for Everyday Stroke Rehabilitation

In this paper, we present a novel smartphone-based rehabilitation approach called ARMStrokes that provides real-time support for stroke survivors to complete rehabilitation exercises for upper extremity recovery. ARMStrokes allows stroke survivors to exercise through interactive games anytime and anywhere and receive instant feedback about the quality of their performance. Stroke survivors can also communicate with their therapists or physicians through the supporting web-based platform. Focus groups involving stroke survivors, caregivers, and therapists have been conducted to evaluate the system and the feedback is highly positive.

Integrating complementary/contradictory information into fuzzy-based VoI determinations

In todays military environment vast amounts of disparate information are available. To aid situational awareness it is vital to have some way to judge information importance. Recent research has developed a fuzzy-based system to assign a Value of Information (VoI) determination for individual pieces of information. This paper presents an investigation of the effect of integrating subsequent complementary and/or contradictory information into the VoI process. Specifically, the idea of using complementary and/or contradictory new information to impact the previously used fuzzy membership values for the information content characteristic applied in the VoI calculations is shown to be a particularly suitable approach.

Ambient Intelligence Based Context-Aware Assistive System to Improve Independence for People with Autism Spectrum Disorder

Individuals with Autism Spectrum Disorder (ASD) can face great challenges in learning and maintaining basic living skills. This not only reduces their possibilities of independent living and employment, but also continuously brings social and financial burdens to their caregivers/mentors. Although research has been proposed to help autism users, most of them focus on improving social and communication skills or providing help for emergency situations. In this paper, we propose a novel portable context-aware assistive system to help autism users in their daily activities such as cooking and cleaning. To make it easily accessible and cost effective, we employ mobile devices and cheap context sensors. Our care system has been implemented and tested under different settings, and a user study involving ten pairs of autism users and their caregiver/mentors has been carried out to evaluate our approach. User feedback is highly positive.

Mining Semantic Time Period Similarity in Spatio-Temporal Climate Data

Over the last decade, advances in high performance computing and remote sensing have produced a vast amount of spatio-temporal data. One area that this data explosion is most prevalent is climate science. With this in mind, there is an increasing need to characterize large spatio-temporal datasets. One such characterization is to find periods of time that exhibit the same spatio-temporal pattern. The focus of this research is to find similar spatio-temporal patterns for semantic time periods. A semantic time period could be any arbitrary division in time such as year, month, or week. The proposed approach first characterizes the data spatially by using one of three approaches including local entropy, local spatial autocorrelation, and local distance-based outliers, to identify interesting spatial features in the dataset. Then, a location/time period matrix which is analogous to a term/document matrix in natural language processing is created to capture the spatial features for a given semantic time period. This matrix contains a count of for each spatial location, the number of times that it is a feature of interest during a semantic time period. Then using latent semantic analysis, the cosine similarity for each semantic time period is calculated. The results are then clustered using affinity propagation. The results show that the similarity matrix produced by distance-based outliers creates the best clustering. The approach is demonstrated on a modeled global climate dataset where we clustered years from 1948 to 2012.

Supporting Stroke Motor Recovery Through a Mobile Application: A Pilot Study

Neuroplasticity and motor learning are promoted with repetitive movement, appropriate challenge, and performance feedback. ARMStrokes, a smartphone application, incorporates these qualities to support motor recovery. Engaging exercises are easily accessible for improved compliance. In a multiple-case, mixed-methods pilot study, the potential of this technology for stroke motor recovery was examined. Exercises calibrated to the participant’s skill level targeted forearm, elbow, and shoulder motions for a 6-wk protocol. Visual, auditory, and vibration feedback promoted self-assessment. Pre- and posttest data from 6 chronic stroke survivors who used the app in different ways (i.e., to measure active or passive motion, to track endurance) demonstrated improvements in accuracy of movements, fatigue, range of motion, and performance of daily activities. Statistically significant changes were not obtained with this pilot study. Further study on the efficacy of this technology is supported.

SQL or NoSQL? Which Is the Best Choice for Storing Big Spatio-Temporal Climate Data?

Management of big spatio-temporal data such as the results from large scale global climate models has long been a challenge because of the sheer vastness of the dataset. Although different data management systems like that incorporate a relational database management system have been proposed and widely used in prior studies, solutions that are particularly designed for big spatio-temporal data management have not been studied well. In this paper, we propose a general data management platform for high-dimensional spatio-temporal datasets like those found in the climate domain, where different database systems can be applied. Through this platform, we compare and evaluate several database systems including SQL database and NoSQL database from various aspects and explore the key impact factors for system performance. Our experimental results indicate advantages and disadvantages of each database system and give insight into the best system to use for big spatio-temporal data applications. Our analysis provides important insights into the understanding of performance of different data management systems, which is very useful for designing high dimensional big data applications.

An Exploratory Case Study to Support Young Children with Spinal Muscular Atrophy (SMA)

In this paper, we describe a preliminary case study that examines the challenges faced by very young children with Type I Spinal Muscular Atrophy (SMA) and how technology may help these children live a more independent life. Several input solutions were examined to support interaction between a young patient and computer-based systems. We started working with the patient when he was two and a half years old. The challenges observed and lessons learned regarding both working with very young children with severe disabilities and the use of specific technical solutions are discussed.